ArticleSensors (Basel, Switzerland)2021
Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Beyond fingerprint and voice print: cough sequence sound for identity verification.npj health systems · 2026Article
- Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring.Scientific reports · 2026Article
- Novel deep learning-based optimization framework for the classification of respiratory diseases using lung sound analysis.Scientific reports · 2026Article
- Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish.BMC medical informatics and decision making · 2025Article
- Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Flow prediction in sound-based uroflowmetry.Scientific reports · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Voice Analysis and Neural Networks as a Clinical Decision Support System for Patients With Lung Diseases.Mayo Clinic proceedings. Digital health · 2024Article
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers.Chinese medicine · 2024Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.IEEE journal of biomedical and health informatics · 2024Article
- Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.PloS one · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician's diagnosis. The chosen model is a bidirectional long-short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs-healthy or pathology (in general or belonging to a specific respiratory pathology)-reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians' diagnosis. To classify the subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.